This notebook contains a set of analyses for analyzing theDL’s boardgamegeek collection. The bulk of the analysis is focused on building a user-specific predictive model to predict the games that the specified user is likely to own. This enables us to ask questions like, based on the games the user currently owns, what games are a good fit for their collection? What upcoming games are they likely to purchase?
We can look at a basic description of the number of games that the user owns, has rated, has previously owned, etc.
What years has the user owned/rated games from? While we can’t see when a user added or removed a game from their collection, we can look at their collection by the years in which their games were published.
We can look at the most frequent types of categories, mechanics, designers, and artists that appear in a user’s collection.
We’ll examine predictive models trained on a user’s collection for games published through 2020. How many games has the user owned/rated/played in the training set (games prior to 2020)?
username | dataset | period | games_owned | games_rated |
theDL | training | published before 2020 | 76 | 82 |
theDL | validation | published 2020 | 13 | 9 |
theDL | test | published after 2020 | 3 | 1 |
The main outcome we will be modeling for the user is owned, which refers to whether the user currently owns or has a previously owned a game in their collection. Our goal is to train a predictive model to learn the probability that a user will add a game to their collection based on its observable features. This amounts to looking at historical data and looking to find patterns that exist between features of games and games present in the user’s collection.
One of the models we trained was a decision tree, which looks for decision rules that can be used to separate games the user owns from games they don’t. The resulting model produces a decision corresponding to yes or no statements: to explain why the model predicts the user to own game, we start at the top of the tree and follow the rules that were learned from the training data.
Note: the tree below has been further pruned to make it easier to visualize.
Decision trees are highly interpretible models that are easy to train and can identify important interactions and nonlinearities present in the data. Individual trees have the drawback of being less predictive than other common models, but it can be useful to look at them to gain some understanding of key predictors and relationships found in the training data.
We can examine coefficients from another model we trained, which is a logistic regression with elastic net regularization (which I will refer to as a penalized logistic regression). Positive values indicate that a feature increases a user’s probability of owning/rating a game, while negative values indicate a feature decreases the probability. To be precise, the coefficients indicate the effect of a particular feature on the log-odds of a user owning a game.
Why did the model identify these features? We can make density plots of the important features for predicting whether the user owned a game. Blue indicates the density for games owned by the user, while grey indicates the density for games not owned by the user.
Binary predictors can be difficult to see with this visualization, so we can also directly examine the percentage of games in a user’s collection with a predictor vs the percentage of all games with that predictor.
% of Games with Feature | ||||
username | Feature | User_Collection | All_Games | Ratio |
theDL | Pandasaurus Games | 2.6% | 0.2% | 12.66 |
theDL | Artist Klemens Franz | 5.3% | 0.6% | 8.72 |
theDL | Pegasus Spiele | 14.5% | 2.2% | 6.62 |
theDL | City Building | 11.8% | 2.3% | 5.18 |
theDL | Games With Solitaire Rules | 26.3% | 5.2% | 5.08 |
theDL | Asmodee | 13.2% | 2.6% | 5.05 |
theDL | Open Drafting | 40.8% | 8.3% | 4.94 |
theDL | Food Cooking | 5.3% | 1.2% | 4.44 |
theDL | Take That | 22.4% | 5.1% | 4.40 |
theDL | Network And Route Building | 10.5% | 2.5% | 4.19 |
theDL | Combinatorial | 5.3% | 1.6% | 3.21 |
theDL | Medieval | 14.5% | 4.6% | 3.13 |
theDL | Animals | 17.1% | 6.1% | 2.81 |
theDL | Set Collection | 36.8% | 13.3% | 2.78 |
theDL | Abstract Strategy | 13.2% | 7.2% | 1.82 |
theDL | Card Game | 52.6% | 29.4% | 1.79 |
Before predicting games in upcoming years, we can examine how well the model did and what games it liked in the training set. In this case, we used resampling techniques (cross validation) to ensure that the model had not seen a game before making its predictions.
Displaying the 100 games from the training set with the highest probability of ownership, highlighting in blue games the user has owned.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2019 | 286096 | Tapestry | 0.792 | no |
2 | 2018 | 199792 | Everdell | 0.564 | yes |
3 | 2018 | 233080 | Book of Dragons | 0.560 | no |
4 | 2014 | 154203 | Imperial Settlers | 0.520 | no |
5 | 2016 | 169786 | Scythe | 0.432 | no |
6 | 2018 | 244711 | Newton | 0.427 | no |
7 | 2015 | 175878 | 504 | 0.423 | no |
8 | 2014 | 160499 | King of New York | 0.283 | no |
9 | 2017 | 229006 | SpyNet | 0.274 | no |
10 | 2012 | 124742 | Android: Netrunner | 0.259 | no |
11 | 2019 | 270971 | Era: Medieval Age | 0.252 | no |
12 | 2018 | 205896 | Rising Sun | 0.230 | no |
13 | 2009 | 54998 | Cyclades | 0.197 | no |
14 | 2014 | 148228 | Splendor | 0.193 | no |
15 | 2007 | 31260 | Agricola | 0.192 | no |
16 | 2011 | 70919 | Takenoko | 0.191 | no |
17 | 2013 | 143693 | Glass Road | 0.181 | no |
18 | 2017 | 197376 | Charterstone | 0.177 | no |
19 | 2017 | 174430 | Gloomhaven | 0.177 | no |
20 | 2014 | 157354 | Five Tribes | 0.172 | no |
21 | 2014 | 159508 | AquaSphere | 0.168 | no |
22 | 2007 | 28143 | Race for the Galaxy | 0.166 | no |
23 | 2010 | 25292 | Merchants & Marauders | 0.161 | no |
24 | 2015 | 182189 | Treasure Hunter | 0.160 | no |
25 | 2019 | 276025 | Maracaibo | 0.159 | no |
26 | 2013 | 52461 | Legacy: The Testament of Duke de Crecy | 0.158 | no |
27 | 2019 | 266507 | Clank!: Legacy – Acquisitions Incorporated | 0.157 | no |
28 | 2017 | 184921 | Bunny Kingdom | 0.150 | no |
29 | 2004 | 9209 | Ticket to Ride | 0.150 | no |
30 | 2019 | 283294 | Yukon Airways | 0.146 | no |
31 | 2019 | 283863 | The Magnificent | 0.145 | no |
32 | 2018 | 247236 | Duelosaur Island | 0.143 | yes |
33 | 2018 | 245638 | Coimbra | 0.141 | no |
34 | 2017 | 216658 | Smash Up: What Were We Thinking? | 0.141 | no |
35 | 2008 | 33107 | Senji | 0.139 | no |
36 | 2019 | 265736 | Tiny Towns | 0.137 | yes |
37 | 2006 | 21654 | Iliad | 0.137 | no |
38 | 2016 | 205398 | Citadels | 0.136 | no |
39 | 2006 | 21348 | Ticket to Ride: Märklin | 0.134 | no |
40 | 2019 | 257066 | Sierra West | 0.134 | no |
41 | 2014 | 132531 | Roll for the Galaxy | 0.128 | no |
42 | 2010 | 70512 | Luna | 0.121 | no |
43 | 2010 | 62219 | Dominant Species | 0.119 | no |
44 | 2018 | 258036 | Between Two Castles of Mad King Ludwig | 0.115 | no |
45 | 2017 | 201825 | Ex Libris | 0.114 | no |
46 | 2018 | 257501 | KeyForge: Call of the Archons | 0.111 | no |
47 | 2019 | 281946 | Aftermath | 0.110 | no |
48 | 2016 | 200147 | Kanagawa | 0.109 | no |
49 | 2017 | 221194 | Dinosaur Island | 0.109 | no |
50 | 2019 | 264220 | Tainted Grail: The Fall of Avalon | 0.109 | no |
51 | 2019 | 284818 | Caylus 1303 | 0.103 | no |
52 | 2019 | 217576 | Hellenica: Story of Greece | 0.102 | no |
53 | 2014 | 150926 | Roll Through the Ages: The Iron Age | 0.101 | no |
54 | 2017 | 220308 | Gaia Project | 0.101 | no |
55 | 2016 | 167791 | Terraforming Mars | 0.101 | no |
56 | 2019 | 276894 | Ticket to Ride: London | 0.101 | no |
57 | 2015 | 172386 | Mombasa | 0.100 | no |
58 | 2010 | 73439 | Troyes | 0.100 | no |
59 | 2016 | 176371 | Explorers of the North Sea | 0.100 | no |
60 | 2016 | 187645 | Star Wars: Rebellion | 0.100 | no |
61 | 2011 | 96848 | Mage Knight Board Game | 0.099 | no |
62 | 2016 | 205637 | Arkham Horror: The Card Game | 0.098 | no |
63 | 2011 | 70149 | Ora et Labora | 0.096 | no |
64 | 2016 | 198487 | Smash Up: Cease and Desist | 0.094 | no |
65 | 2014 | 156336 | Onirim (Second Edition) | 0.093 | no |
66 | 2019 | 266192 | Wingspan | 0.093 | yes |
67 | 2012 | 119391 | Il Vecchio | 0.092 | no |
68 | 2012 | 122522 | Smash Up | 0.090 | no |
69 | 2009 | 58798 | Cardcassonne | 0.089 | no |
70 | 2015 | 163967 | Tiny Epic Galaxies | 0.088 | no |
71 | 2017 | 234487 | Altiplano | 0.088 | no |
72 | 2019 | 251551 | Dale of Merchants Collection | 0.087 | yes |
73 | 2016 | 201808 | Clank!: A Deck-Building Adventure | 0.087 | no |
74 | 2019 | 266810 | Paladins of the West Kingdom | 0.087 | no |
75 | 2007 | 31627 | Ticket to Ride: Nordic Countries | 0.086 | no |
76 | 2004 | 9202 | Saga | 0.086 | no |
77 | 2012 | 121921 | Robinson Crusoe: Adventures on the Cursed Island | 0.083 | no |
78 | 2018 | 262215 | Blackout: Hong Kong | 0.082 | no |
79 | 2018 | 256226 | Azul: Stained Glass of Sintra | 0.080 | no |
80 | 2012 | 117915 | Yedo | 0.078 | no |
81 | 2017 | 224815 | Deca Slayer | 0.078 | no |
82 | 2016 | 191977 | The Castles of Burgundy: The Card Game | 0.076 | no |
83 | 2019 | 251247 | Barrage | 0.076 | no |
84 | 2018 | 214032 | Founders of Gloomhaven | 0.075 | no |
85 | 2013 | 124361 | Concordia | 0.074 | no |
86 | 2017 | 195539 | The Godfather: Corleone's Empire | 0.074 | no |
87 | 2019 | 281259 | The Isle of Cats | 0.073 | yes |
88 | 2016 | 195856 | Bloodborne: The Card Game | 0.073 | no |
89 | 2013 | 138233 | Ascension: Rise of Vigil | 0.073 | no |
90 | 2017 | 179172 | Unfair | 0.071 | no |
91 | 2016 | 177736 | A Feast for Odin | 0.071 | no |
92 | 2018 | 258140 | Les Aventuriers du Rail Express | 0.070 | no |
93 | 2009 | 39683 | At the Gates of Loyang | 0.069 | no |
94 | 2013 | 133848 | Euphoria: Build a Better Dystopia | 0.068 | no |
95 | 2018 | 214989 | Age of Towers | 0.067 | no |
96 | 2019 | 285775 | KeyForge: Worlds Collide | 0.067 | no |
97 | 2012 | 122515 | Keyflower | 0.067 | no |
98 | 2019 | 274364 | Watergate | 0.066 | no |
99 | 2012 | 128780 | Pax Porfiriana | 0.066 | no |
100 | 2019 | 254192 | Harry Potter: Hogwarts Battle – Defence Against the Dark Arts | 0.065 | no |
This section contains a variety of visualizations and metrics for assessing the performance of the model(s) during resampling. If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.
An easy way to examine the performance of classification model is to view a separation plot. We plot the predicted probabilities from the model for every game (from resampling) from lowest to highest. We then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (right side of the chart).
We can more formally assess how well each model did in resampling by looking at the area under the receiver operating characteristic curve. A perfect model would receive a score of 1, while a model that cannot predict the outcome will default to a score of 0.5. The extent to which something is a good score depends on the setting, but generally anything in the .8 to .9 range is very good while the .7 to .8 range is perfectly acceptable.
wflow_id | .metric | .estimator | .estimate |
GLM | roc_auc | binary | 0.89 |
Decision Tree | roc_auc | binary | 0.73 |
Another way to think about the model performance is to view its lift, or its ability to detect the positive outcomes over that of a null model. High lift indicates the model can much more quickly find all of the positive outcomes (in this case, games owned or played by the user), while a model with no lift is no better than random guessing. A gains chart is another way to view this.
While we are probably more interested in the lift provided by the models to evaluate their efficacy, we can also explore the optimal cutpoint if we wanted to define a hard threshold for identifying games a user will own vs not own.
The threshold we select depends on how we much we care about false positives (games the model predicts that the user does not own) vs false negatives (games the user owns that the model does not predict). We can toggle threshold to
Finally, we can understand the performance of the model by examining its calibration. If the model assigns a probability of 5%, how often does the outcome actually occur? A well calibrated model is one in which the predicted probabilities reflect the probabilities we would observe in the actual data. We can assess the calibration of a model by grouping its predictions into bins and assessing how often we observe the outcome versus how often our model expects to observe the outcome.
A model that is well calibrated will closely follow the dashed line - its expected probabilities match that of the observed probabilities. A model that consistently underestimates the probability of the event will be over this dashed line, be a while a model that overestimates the probability will be under the dashed line.
What games does the model think theDL is most likely to own that are not in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2019 | 286096 | Tapestry | 0.792 | no |
2018 | 233080 | Book of Dragons | 0.560 | no |
2014 | 154203 | Imperial Settlers | 0.520 | no |
2016 | 169786 | Scythe | 0.432 | no |
2018 | 244711 | Newton | 0.427 | no |
What games does the model think theDL is least likely to own that are in their collection?
Published | ID | Name | Pr(Owned) | Owned |
1999 | 1353 | Time's Up! | 0.001 | yes |
2019 | 275913 | Bruxelles 1897 | 0.001 | yes |
2019 | 277085 | Love Letter | 0.002 | yes |
2014 | 169654 | Deep Sea Adventure | 0.002 | yes |
2017 | 197443 | Fugitive | 0.002 | yes |
Top 25 games most likely to be owned by the user in each year, highlighting in blue the games that the user has owned.
rank | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
1 | Android: Netrunner | Glass Road | Imperial Settlers | 504 | Scythe | SpyNet | Everdell | Tapestry |
2 | Il Vecchio | Legacy: The Testament of Duke de Crecy | King of New York | Treasure Hunter | Citadels | Charterstone | Book of Dragons | Era: Medieval Age |
3 | Smash Up | Concordia | Splendor | Mombasa | Kanagawa | Gloomhaven | Newton | Maracaibo |
4 | Robinson Crusoe: Adventures on the Cursed Island | Ascension: Rise of Vigil | Five Tribes | Tiny Epic Galaxies | Terraforming Mars | Bunny Kingdom | Rising Sun | Clank!: Legacy – Acquisitions Incorporated |
5 | Yedo | Euphoria: Build a Better Dystopia | AquaSphere | Grand Austria Hotel | Explorers of the North Sea | Smash Up: What Were We Thinking? | Duelosaur Island | Yukon Airways |
6 | Keyflower | Ghooost! | Roll for the Galaxy | Sylvion | Star Wars: Rebellion | Ex Libris | Coimbra | The Magnificent |
7 | Pax Porfiriana | Lewis & Clark: The Expedition | Roll Through the Ages: The Iron Age | The Game | Arkham Horror: The Card Game | Dinosaur Island | Between Two Castles of Mad King Ludwig | Tiny Towns |
8 | Tokaido | Cappuccino | Onirim (Second Edition) | SteamRollers | Smash Up: Cease and Desist | Gaia Project | KeyForge: Call of the Archons | Sierra West |
9 | Love Letter | Forbidden Desert | Viceroy | Dale of Merchants | Clank!: A Deck-Building Adventure | Altiplano | Blackout: Hong Kong | Aftermath |
10 | Zug um Zug: Deutschland | Rococo | Port Royal | Ascension: Dreamscape | The Castles of Burgundy: The Card Game | Deca Slayer | Azul: Stained Glass of Sintra | Tainted Grail: The Fall of Avalon |
11 | Ascension: Immortal Heroes | Ascension: Darkness Unleashed | Three Kingdoms Redux | Ascension: Dawn of Champions | Bloodborne: The Card Game | The Godfather: Corleone's Empire | Founders of Gloomhaven | Caylus 1303 |
12 | The Great Zimbabwe | Gravwell: Escape from the 9th Dimension | Spurs: A Tale in the Old West | Bullfrogs | A Feast for Odin | Unfair | Les Aventuriers du Rail Express | Hellenica: Story of Greece |
13 | Suburbia | Munchkin Legends | Ascension: Realms Unraveled | Elysium | Black Orchestra | Queendomino | Age of Towers | Ticket to Ride: London |
14 | Würfelwurst | Patchistory | Nations: The Dice Game | Pirates of the 7 Seas | Aeon's End | Tybor the Builder | Architects of the West Kingdom | Wingspan |
15 | Rattus Cartus | Guildhall: Job Faire | Legendary Encounters: An Alien Deck Building Game | Between Two Cities | Machi Koro: Bright Lights, Big City | Breaking Bad: The Board Game | Renegade | Dale of Merchants Collection |
16 | Seasons | Monster City Planners | Roll Through the Ages: The Iron Age with Mediterranean Expansion | Cat Tower | Port Royal: Unterwegs! | Twilight Imperium: Fourth Edition | Robin Hood and the Merry Men | Paladins of the West Kingdom |
17 | Libertalia | Hobbit Tales from the Green Dragon Inn | Valley of the Kings | Lost Legacy: Second Chronicle – Vorpal Sword & Whitegold Spire | Greedy Greedy Goblins | Dragon Castle | New Frontiers | Barrage |
18 | Terra Mystica | Firefly: The Game | Fields of Arle | Taste of Poland | Pandemic: Iberia | 878 Vikings: Invasions of England | Underwater Cities | The Isle of Cats |
19 | Guildhall | City of Iron | DungeonQuest Revised Edition | Ferox | Covert | TRUT | Concordia Venus | KeyForge: Worlds Collide |
20 | Ginkgopolis | Impulse | Subdivision | Heroes | Dream Home | My Little Scythe | Lords of Hellas | Watergate |
21 | Agricola: All Creatures Big and Small | Sushi Go! | Orléans | FUSE | Agricola (Revised Edition) | Food Truck Champion | Valhalla | Harry Potter: Hogwarts Battle – Defence Against the Dark Arts |
22 | Micro Monsters | Craftsmen | Sheriff of Nottingham | The Bloody Inn | Ascension X: War of Shadows | Circle the Wagons | Root | Tanuki Market |
23 | Kemet | Room 25 | Star Realms | Munchkin Christmas Lite | Dokmus | Ascension: Gift of the Elements | Hokkaido | To the Death! |
24 | Snowdonia | Relic Runners | Tiny Epic Kingdoms | The Builders: Antiquity | Turin Market | Sagrada | No Honor Among Thieves | Aeon's End: Legacy |
25 | Uchronia | The Builders: Middle Ages | Belle of the Ball | Baseball Highlights: 2045 | Heir to the Pharaoh | Century: Spice Road | The Pirate Republic | Villagers |
This is an interactive table for the model’s predictions for the training set (from resampling).
We’ll validate the model by looking at its predictions for games published in 2020. That is, how well did a model trained on a user’s collection through 2020 perform in predicting games for the user in 2020?
username | outcome | dataset | method | .metric | .estimate |
theDL | owned | validation | GLM | roc_auc | 0.785 |
theDL | owned | validation | Decision Tree | roc_auc | 0.689 |
Table of top 50 games from 2020, highlighting games that the user owns.
Published | ID | Name | Pr(Owned) | Owned |
2020 | 292333 | Cowboys II: Cowboys & Indians Edition | 0.230 | no |
2020 | 298371 | Wild Space | 0.158 | no |
2020 | 296626 | Sonora | 0.138 | no |
2020 | 316554 | Dune: Imperium | 0.085 | yes |
2020 | 316412 | The LOOP | 0.079 | no |
2020 | 256317 | Guild Master | 0.077 | no |
2020 | 184267 | On Mars | 0.074 | no |
2020 | 283155 | Calico | 0.073 | no |
2020 | 299452 | Dale of Merchants 3 | 0.068 | yes |
2020 | 291457 | Gloomhaven: Jaws of the Lion | 0.066 | no |
2020 | 306481 | Tawantinsuyu: The Inca Empire | 0.064 | no |
2020 | 296151 | Viscounts of the West Kingdom | 0.063 | no |
2020 | 319966 | The King Is Dead: Second Edition | 0.060 | no |
2020 | 293141 | King of Tokyo: Dark Edition | 0.057 | no |
2020 | 306040 | Merv: The Heart of the Silk Road | 0.057 | no |
2020 | 309113 | Ticket to Ride: Amsterdam | 0.056 | no |
2020 | 233262 | Tidal Blades: Heroes of the Reef | 0.050 | no |
2020 | 318983 | Faiyum | 0.048 | no |
2020 | 301607 | KeyForge: Mass Mutation | 0.045 | no |
2020 | 300322 | Hallertau | 0.044 | no |
2020 | 262208 | Dungeon Drop | 0.043 | no |
2020 | 256940 | Krosmaster: Blast | 0.043 | no |
2020 | 308755 | Ascension: Eternal | 0.042 | no |
2020 | 299252 | Here to Slay | 0.041 | no |
2020 | 299179 | Chancellorsville 1863 | 0.040 | no |
2020 | 276205 | Philosophia: Dare to be Wise | 0.039 | no |
2020 | 284742 | Honey Buzz | 0.036 | no |
2020 | 293678 | Stellar | 0.035 | no |
2020 | 293556 | Gloomy Graves | 0.034 | no |
2020 | 320505 | Mattock | 0.034 | no |
2020 | 270109 | Iwari | 0.032 | no |
2020 | 318084 | Furnace | 0.030 | no |
2020 | 262274 | D6: Dungeons, Dudes, Dames, Danger, Dice and Dragons! | 0.030 | no |
2020 | 306735 | Under Falling Skies | 0.029 | no |
2020 | 289939 | Goblin Teeth | 0.029 | no |
2020 | 316622 | Gods Love Dinosaurs | 0.028 | no |
2020 | 299074 | Space Battle Lunchtime Card Game | 0.027 | no |
2020 | 286749 | Hansa Teutonica: Big Box | 0.027 | yes |
2020 | 266188 | Seven Bridges | 0.027 | no |
2020 | 301716 | Glasgow | 0.027 | no |
2020 | 236713 | Sea of Plunder | 0.027 | no |
2020 | 299592 | Beez | 0.026 | no |
2020 | 299317 | Aeon's End: Outcasts | 0.026 | no |
2020 | 312804 | Pendulum | 0.026 | yes |
2020 | 282922 | Windward | 0.025 | no |
2020 | 246900 | Eclipse: Second Dawn for the Galaxy | 0.024 | no |
2020 | 312346 | Munchkin Disney | 0.024 | no |
2020 | 232414 | Oceans | 0.023 | yes |
2020 | 282246 | Camp Pinetop | 0.023 | no |
2020 | 309110 | Food Chain Island | 0.023 | no |
We can then refit our model to the training and validation set in order to predict all upcoming games for the user.
Examine the top 100 upcoming games, highlighting in blue ones the user already owns.
Published | ID | Name | Pr(Owned) | Owned |
2021 | 339906 | The Hunger | 0.327 | no |
2022 | 331106 | The Witcher: Old World | 0.310 | no |
2021 | 329465 | Red Rising | 0.293 | no |
2022 | 310873 | Carnegie | 0.186 | no |
2022 | 349793 | Age of Rome | 0.136 | no |
2021 | 301366 | Caves of Rwenzori | 0.108 | no |
2021 | 298102 | Roll Camera!: The Filmmaking Board Game | 0.079 | no |
2021 | 310641 | Ostium | 0.068 | no |
2021 | 307862 | Dollars to Donuts | 0.068 | no |
2021 | 211364 | Seize the Bean | 0.065 | no |
2021 | 324242 | Sheepy Time | 0.063 | no |
2022 | 251661 | Oathsworn: Into the Deepwood | 0.063 | no |
2021 | 340834 | Gravwell: 2nd Edition | 0.055 | no |
2021 | 314491 | Meadow | 0.055 | no |
2021 | 306202 | Philosophia: Floating World | 0.054 | no |
2021 | 346603 | Hungry Little Demons | 0.052 | no |
2022 | 345584 | Mindbug | 0.050 | no |
2021 | 290236 | Canvas | 0.050 | no |
2023 | 347909 | Rogue Angels: Legacy of the Burning Suns | 0.049 | no |
2022 | 295770 | Frosthaven | 0.048 | no |
2021 | 257706 | Zoo-ography | 0.048 | no |
2021 | 280984 | Ruins: Death Binder | 0.048 | no |
2021 | 319793 | Happy City | 0.047 | no |
2021 | 295947 | Cascadia | 0.047 | yes |
2021 | 344258 | That Time You Killed Me | 0.047 | yes |
2021 | 334782 | Bayou Bash | 0.046 | no |
2022 | 356033 | Libertalia: Winds of Galecrest | 0.046 | no |
2021 | 342942 | Ark Nova | 0.046 | no |
2021 | 291572 | Oath: Chronicles of Empire and Exile | 0.044 | no |
2021 | 341169 | Great Western Trail (Second Edition) | 0.042 | no |
2021 | 260524 | Beyond Humanity: Colonies | 0.041 | no |
2021 | 332944 | Sobek: 2 Players | 0.040 | no |
2021 | 316080 | KeyForge: Dark Tidings | 0.040 | no |
2021 | 316625 | Cafe Chaos | 0.039 | no |
2021 | 249277 | Brazil: Imperial | 0.039 | no |
2021 | 305682 | Rolling Realms | 0.038 | no |
2022 | 304051 | Creature Comforts | 0.037 | no |
2021 | 338980 | Eastern Empires | 0.037 | no |
2021 | 314393 | Wutaki | 0.036 | no |
2021 | 331635 | Kameloot | 0.036 | no |
2022 | 294880 | Chai: Tea for 2 | 0.036 | no |
2021 | 314088 | Agropolis | 0.035 | no |
2022 | 305096 | Endless Winter: Paleoamericans | 0.035 | no |
2021 | 313841 | Lunar Base | 0.034 | no |
2021 | 336195 | League of Dungeoneers | 0.034 | no |
2021 | 317457 | Dinosaur World | 0.033 | no |
2021 | 322014 | All-Star Draft | 0.033 | no |
2021 | 303954 | Pax Viking | 0.033 | no |
2021 | 340420 | Throw Throw Avocado | 0.033 | no |
2022 | 273814 | Deliverance | 0.032 | no |
2021 | 343905 | Boonlake | 0.032 | no |
2022 | 324517 | Zodiac War | 0.032 | no |
2021 | 331212 | Aeon's End: Legacy of Gravehold | 0.031 | no |
2021 | 283387 | Rocketmen | 0.030 | no |
2021 | 342246 | Feuding Foodies | 0.030 | no |
2021 | 328479 | Living Forest | 0.029 | no |
2021 | 337787 | Summer Camp | 0.029 | no |
2021 | 344277 | Corrosion | 0.029 | no |
2022 | 258779 | Planet Unknown | 0.028 | no |
2022 | 334065 | Verdant | 0.028 | no |
2021 | 344768 | Mobile Markets: A Smartphone Inc. Game | 0.027 | no |
2022 | 322524 | Bardsung | 0.027 | no |
2021 | 304985 | Dark Ages: Holy Roman Empire | 0.027 | no |
2021 | 295535 | Dark Ages: Heritage of Charlemagne | 0.027 | no |
2022 | 331398 | Mythic Battles: Ragnarök | 0.026 | no |
2021 | 340237 | Wonder Book | 0.026 | no |
2021 | 348461 | Castle Break | 0.026 | no |
2022 | 325810 | Ascension: 10 Year Anniversary Edition | 0.026 | no |
2021 | 339789 | Welcome to the Moon | 0.026 | no |
2021 | 282700 | LOOP: Life of Ordinary People | 0.025 | no |
2021 | 319792 | Fly-A-Way | 0.025 | no |
2022 | 349067 | The Lord of the Rings: The Card Game – Revised Core Set | 0.025 | no |
2022 | 319807 | Shogun no Katana | 0.024 | no |
2021 | 333055 | Subastral | 0.024 | no |
2021 | 332386 | Brew | 0.023 | no |
2022 | 317511 | Tindaya | 0.023 | no |
2021 | 325829 | Let's Summon Demons | 0.022 | no |
2021 | 323601 | Potato Inferno! | 0.021 | no |
2021 | 339905 | Love Letter: Princess Princess Ever After | 0.021 | no |
2022 | 342444 | Black Rose Wars: Rebirth | 0.021 | no |
2021 | 300148 | Spy Connection | 0.021 | no |
2021 | 298069 | Cubitos | 0.021 | no |
2021 | 339484 | Savannah Park | 0.021 | no |
2021 | 262941 | Dominant Species: Marine | 0.021 | no |
2022 | 335427 | Wild: Serengeti | 0.021 | no |
2021 | 325414 | Happy Little Dinosaurs | 0.020 | no |
2022 | 318838 | Quests & Cannons: The Risen Islands | 0.020 | no |
2021 | 346296 | Tic Tac K.O.: Dragons vs Unicorns | 0.020 | no |
2021 | 275061 | Rulebenders | 0.020 | no |
2022 | 338460 | The Isle of Cats: Explore & Draw | 0.020 | no |
2022 | 344839 | Dog Lover | 0.020 | no |
2021 | 329670 | Pandemic: Hot Zone – Europe | 0.020 | no |
2021 | 295785 | Euthia: Torment of Resurrection | 0.020 | no |
2021 | 313269 | Rescuing Robin Hood | 0.019 | no |
2021 | 286632 | Blood of the Northmen | 0.019 | no |
2021 | 302914 | Dream Cruise | 0.019 | no |
2021 | 295607 | Canopy | 0.019 | no |
2021 | 277080 | Titans | 0.019 | no |
2022 | 331401 | Dog Park | 0.018 | no |
2021 | 333539 | The Siege of Runedar | 0.018 | no |